# NumPy Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/numpy
> Markdown URL: https://aitinkerers.org/technologies/numpy.md
> Technology record last updated: 2026-09-18T15:13:50Z
> Generated: 2026-09-21T07:42:49Z

NumPy is the fundamental Python package for high-performance scientific computing, centered on the powerful N-dimensional array object (ndarray).

NumPy (Numerical Python) is the foundational library for scientific computing in Python, providing the core `ndarray` (N-dimensional array) object . This object is a homogeneous, fixed-size container that enables highly efficient, vectorized operations: often up to 50x faster than standard Python lists for large datasets . The library includes a comprehensive collection of routines for fast array manipulation, including linear algebra, Fourier transforms, and random simulation . Its performance advantage stems from its core being optimized C and C++ code , establishing it as the universal data structure for data exchange across the entire Python scientific computing ecosystem.

- Official technology site: https://numpy.org
- Public AI Tinkerers demos and talks: 6
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Robot Picks Grapes Based on Spatial World Model](https://seattle.aitinkerers.org/talks/rsvp_hwjgpFnwm2M)

We built a new model that is based on the human brain. Right now it can navigate and reason to play Pac Man but we are working on connecting it to a robot and having it pick grapes.

- Event context: AI Tinkerers Seattle Summer Bash — 2026-07-29 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_hwjgpFnwm2M

### [Building an ML Decision Lab for Agriculture: Turning Predictions into Learning](https://lausanne.aitinkerers.org/talks/rsvp_4vwa4V9v2ek)

FarmBuddy is an interactive machine learning decision-support system built with Streamlit and a Random Forest regression pipeline. Users can modify agricultural inputs such as crop type, fertilizer usage, land area, and season, then observe how model predictions change in real time. Beyond prediction, the system includes decision logging, before-and-after scenario comparison, input validation, and a learning summary layer designed to help users understand how machine learning models respond to changing conditions. During the demo, I will show the live application, model inference workflow, session-state architecture, and the decision comparison engine.

- Event context: AI Tinkerers Lausanne June 2026 Meetup — 2026-06-25 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_4vwa4V9v2ek

### [Engenheiro civil + Claude Code: app de terraplenagem 100% determinístico construído por conversa](https://curitiba.aitinkerers.org/talks/rsvp_iwCDvtHEc-s)

KML Earthworks é um app Streamlit que transforma um traçado de estrada de acesso desenhado no Google Earth em estimativa de terraplenagem (perfil longitudinal, volumes de corte/aterro pela fórmula do prismatoide, balanço de massa, diagrama de Bruckner) em segundos — upload de .kml, download de Excel, zero GIS de desktop. Na demo eu vou: (1) desenhar um acesso ao vivo no Google Earth e rodar o app em produção (kml-earthworks.streamlit.app) mostrando o pipeline completo (parse → stationing a cada 20m → enriquecimento de elevação com fallback de API → otimização de grade → volumes → export); (2) abrir o repo no editor e mostrar a estrutura src/ modular, o CLAUDE.md que funciona como guideline persistente para o agente, e rodar os 58 testes ao vivo no terminal; (3) navegar pelos commits para mostrar a evolução notebook → pacote → app por desenvolvimento conversacional com Claude Code, incluindo os pontos onde eu tive que intervir manualmente.

- Event context: AI Tinkerers Curitiba: Evento Inaugural — 2026-06-10 — Curitiba
- Public talk page: https://curitiba.aitinkerers.org/talks/rsvp_iwCDvtHEc-s

### [Dynamic Variational Autoencoders for Structured Causal Discovery in Time Series](https://toronto.aitinkerers.org/talks/rsvp_rJPf0FX5JH4)

This talk introduces a novel framework for learning dynamic causal relationships in multivariate time series using a Variational Autoencoder (VAE) architecture. The model integrates recurrent and probabilistic components to jointly capture temporal dependencies and causal structure, addressing limitations of traditional Granger-causality and constraint-based approaches.

- Event context: AI Tinkerers Toronto - December Meetup sponsored by Auth0 and TribalScale! — 2025-12-03 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_rJPf0FX5JH4

### [IA + ML + Quantum Classification with IBM Quantum Computing](https://pereira.aitinkerers.org/talks/rsvp_3BaPmpS9T3E)

En este proyecto exploramos el uso de algoritmos de clasificación implementados con IBM Quantum Computing utilizando circuitos cuánticos de qubits. Contrastamos su rendimiento con la versión clásica del algoritmo para medir tiempos y eficiencia. Durante la experimentación descubrimos que el mayor reto no estaba en la cuántica en sí, sino en cómo se construían los cálculos: crear un circuito y un statevector para cada punto y cada centroide volvía el proceso ineficiente en Python. La solución fue usar un feature map simple (rotaciones RY sin entrelazamiento), que permite calcular la fidelidad cuántica mediante una fórmula cerrada y vectorización con NumPy, evitando la construcción de circuitos en cada paso. Esto transformó el cuello de botella en operaciones matriciales rápidas (cos, mul, argmax) y permitió comparar de manera más justa contra KMeans de scikit-learn.

- Event context: AI Tinkerers Pereira: De Usuarios a Makers: Prototipos que Inspiran — 2025-09-24 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_3BaPmpS9T3E

### [vlite 2](https://palo-alto.aitinkerers.org/talks/rsvp_1s0T3zKJ0H4)

a simple and blazing fast vector database made in numpy

- Event context: AI Tinkerers Palo Alto - Inaugural Meetup — 2024-05-01 — Palo Alto
- Public talk page: https://palo-alto.aitinkerers.org/talks/rsvp_1s0T3zKJ0H4

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